Measurement and study of the distributing law of in-situ stresses in rock mass at great depth

To solve the technical cruxes of the conventional system in deep rock mass, an automatic testing system for hydraulic fracturing that includes a single tube for hydraulic loop, a pressure-relief valve, central-tubeless packers, and a multichannel real-time data acquisition system was used for in-situ stresses measurement at great depths （over 1000 m） in a coalfield in Juye of Northern China. The values and orientations of horizontal principal stresses were determined by the new system. The virgin stress field and its distributing law were decided by the linear regression from the logged 37 points in seven boreholes. Besides, the typical boreholes arranged in both the adjacent zone and far away zone of the faults were analyzed, respectively. The results show that a stress concentration phenomenon and a deflection in the orientation of the maximal horizontal stress exist in the adjacent zone of the faults, which further provides theoretical basis for design and optimization of mining.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

Association of daily sitting time and leisure-time physical activity with body fat among U.S.adults

Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.

The initial stages of Li_(2)O_(2) formation during oxygen reduction reaction in Li-O_(2) batteries:The significance of Li_(2)O_(2) in charge-transfer reactions within devices

Lithium-oxygen batteries are a promising technology because they can greatly surpass the energy density of lithium-ion batteries.However,this theoretical characteristic has not yet been converted into a real device with high cyclability.Problems with air contamination,metallic lithium reactivity,and complex discharge and charge reactions are the main issues for this technology.A fast and reversible oxygen reduction reaction(ORR)is crucial for good performance of secondary batteries',but the partial knowledge of its mechanisms,especially when devices are concerned,hinders further development.From this perspective,the present work uses operando Raman experiments and electrochemical impedance spectroscopy(EIS)to assess the first stages of the discharge processes in porous carbon electrodes,following their changes cycle by cycle at initial operation.A growth kinetic formation of the discharge product signal(Li_(2)O_(2))was observed with operando Raman,indicating a first-order reaction and enabling an analysis by a microkinetic model.The solution mechanism in the evaluated system was ascribed for an equivalent circuit with three time constants.While the time constant for the anode interface reveals to remain relatively constant after the first discharge,its surface seemed to be more non-uniform.The model indicated that the reaction occurs at the Li_(2)O_(2) surface,decreasing the associated resistance during the initial discharge phase.Furthermore,the growth of Li_(2)O_(2) forms a hetero-phase between Li_(2)O_(2)/electrolyte,while creating a more compact and homogeneous on the Li_(2)O_(2)/cathode surface.The methodology here described thus offers a way of directly probing changes in surface chemistry evolution during cycling from a device through EIS analysis.

Effect of dynamic loading orientation on fracture properties of surrounding rocks in twin tunnels

For expedited transportation,vehicular tunnels are often designed as two adjacent tunnels,which frequently experience dynamic stress waves from various orientations during blasting excavation.To analyze the impact of dynamic loading orientation on the stability of the twin-tunnel,a split Hopkinson pressure bar(SHPB)apparatus was used to conduct a dynamic test on the twin-tunnel specimens.The two tunnels were rotated around the specimen’s center to consider the effect of dynamic loading orientation.LS-DYNA software was used for numerical simulation to reveal the failure properties and stress wave propagation law of the twin-tunnel specimens.The findings indicate that,for a twin-tunnel exposed to a dynamic load from different orientations,the crack initiation position appears most often at the tunnel corner,tunnel spandrel,and tunnel floor.As the impact direction is created by a certain angle(30°,45°,60°,120°,135°,and 150°),the fractures are produced in the middle of the line between the left tunnel corner and the right tunnel spandrel.As the impact loading angle(a)is 90°,the tunnel sustains minimal damage,and only tensile fractures form in the surrounding rocks.The orientation of the impact load could change the stress distribution in the twin-tunnel,and major fractures are more likely to form in areas where the tensile stress is concentrated.

Prevalence and ecological factors affecting the distribution of plant-parasitic nematodes in Prunus groves in Spain

A wide survey was conducted to study plant-parasitic nematodes(PPNs)associated with Prunus groves in Spain.This research aimed to determine the prevalence and distribution of PPNs in Prunus groves,as well as the influence of explanatory variables describing soil,climate and agricultural management in structuring the variation of PPNs community composition.A total of 218 sampling sites were surveyed and 84 PPN species belonging to 32 genera were identified based of an integrative taxonomic approach.PPN species considered as potential limiting factors in Prunus production,such as Meloidogyne arenaria,M.incognita,M.javanica,Pratylenchus penetrans and P.vulnus,were identified in this survey.Seven soil physico-chemical(C,Mg,N,Na,OM,P,pH and clay,loamy sand and sandy loam texture classes),four climate(Bio04,Bio05,Bio13 and Bio14)and four agricultural management variables(grove-use history less than 10 years,irrigation,apricot seedling rootstock,and Montclar rootstock)were identified as the most influential variables driving spatial patterns of PPNs communities.In particular,younger plantations showed higher values for species richness and diversity indices than groves cultivated for more than 20 years with Prunus spp.Our study increases the knowledge of the distribution and prevalence of PPNs associated with Prunus rhizosphere,as well as on the influence of explanatory variables driving the spatial structure PPNs communities,which has important implications for the successful design of sustainable management strategies in the future in this agricultural system.

Boosting Adversarial Training with Learnable Distribution

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural networks.Adversarial training is one of the most potent methods to defend against adversarial attacks.However,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training.This paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture model.The distribution centroid is built to classify samples and constrain the distribution of the sample features.The natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the model.The proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial training.This algorithm gradually increases the accuracy and robustness of the model by scaling perturbation.Finally,the proposed method outputs the predicted labels and the distance between the sample and the distribution centroid.The distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model defense.The effectiveness of the proposed method is demonstrated through comprehensive experiments.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Mastering air combat game with deep reinforcement learning

Reinforcement learning has been applied to air combat problems in recent years,and the idea of curriculum learning is often used for reinforcement learning,but traditional curriculum learning suffers from the problem of plasticity loss in neural networks.Plasticity loss is the difficulty of learning new knowledge after the network has converged.To this end,we propose a motivational curriculum learning distributed proximal policy optimization(MCLDPPO)algorithm,through which trained agents can significantly outperform the predictive game tree and mainstream reinforcement learning methods.The motivational curriculum learning is designed to help the agent gradually improve its combat ability by observing the agent's unsatisfactory performance and providing appropriate rewards as a guide.Furthermore,a complete tactical maneuver is encapsulated based on the existing air combat knowledge,and through the flexible use of these maneuvers,some tactics beyond human knowledge can be realized.In addition,we designed an interruption mechanism for the agent to increase the frequency of decisionmaking when the agent faces an emergency.When the number of threats received by the agent changes,the current action is interrupted in order to reacquire observations and make decisions again.Using the interruption mechanism can significantly improve the performance of the agent.To simulate actual air combat better,we use digital twin technology to simulate real air battles and propose a parallel battlefield mechanism that can run multiple simulation environments simultaneously,effectively improving data throughput.The experimental results demonstrate that the agent can fully utilize the situational information to make reasonable decisions and provide tactical adaptation in the air combat,verifying the effectiveness of the algorithmic framework proposed in this paper.

The Uptake and Distribution Evidence of Nano-and Microplastics in vivo after a Single High Dose of Oral Exposure

Objective Tissue uptake and distribution of nano-/microplastics was studied at a single high dose by gavage in vivo.Methods Fluorescent microspheres(100 nm,3μm,and 10μm)were given once at a dose of 200 mg/(kg∙body weight).The fluorescence intensity(FI)in observed organs was measured using the IVIS Spectrum at 0.5,1,2,and 4 h after administration.Histopathology was performed to corroborate these findings.Results In the 100 nm group,the FI of the stomach and small intestine were highest at 0.5 h,and the FI of the large intestine,excrement,lung,kidney,liver,and skeletal muscles were highest at 4 h compared with the control group(P<0.05).In the 3μm group,the FI only increased in the lung at 2 h(P<0.05).In the 10μm group,the FI increased in the large intestine and excrement at 2 h,and in the kidney at 4 h(P<0.05).The presence of nano-/microplastics in tissues was further verified by histopathology.The peak time of nanoplastic absorption in blood was confirmed.Conclusion Nanoplastics translocated rapidly to observed organs/tissues through blood circulation;however,only small amounts of MPs could penetrate the organs.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

Tissue distribution of cadmium and its effect on reproduction in Spodoptera exigua

Vegetable fields are often contaminated by heavy metals,and Spodoptera exigua is a major vegetable pest which is stressed by heavy metals mainly by feeding.In this study,cadmium accumulation in the tissues of S.exigua exposed to cadmium and its effects on the growth and development of the parents and the offspring were investigated.Under the stress of different concentrations of cadmium(0.2,3.2,and 51.2 mg kg^(-1)),the cadmium content in each tissue of S.exigua increased in a dose-dependent manner.At the larval stage,the highest cadmium accumulation was found in midgut in all three cadmium treatments,but at the adult stage,the highest cadmium content was found in fat body.In addition,the cadmium content in ovaries was much higher than in testes.When F1S.exigua was stressed by cadmium and the F_(2)generation was not fed a cadmium-containing diet,the larval survival,pupation rate,emergence rate and fecundity of the F_(2)generation were significantly reduced in the 51.2 mg kg^(-1)treatment compared to the corresponding F1generation.Even in the F_(2)generation of the 3.2 mg kg^(-1)treatment,the fecundity was significantly lower than in the parental generation.The fecundity of the only-female stressed treatment was significantly lower than that of the only-male stressed treatment at the 3.2 and 51.2 mg kg^(-1)cadmium exposure levels.When only mothers were stressed at the larval stage,the fecundity of the F_(2)generation was significantly lower than that of the F1generation in the 51.2 mg kg^(-1)treatment,and it was also significantly lower than in the 3.2 and 0.2 mg kg^(-1)treatments.The results of our study can provide useful information for forecasting the population increase trends under different heavy metal stress conditions and for the reliable environmental risk assessment of heavy metal pollution.

Compartmentalized regulation of organelle integrity in neurodegenerative diseases:lessons from the Drosophila motor neuron

Neurons are highly polarized,morphologically asymmetric,and functionally compartmentalized cells that contain long axons extending from the cell body.For this reason,their maintenance relies on spatiotemporal regulation of organelle distribution between the somatodendritic and axonal domains.Although some organelles,such as mitochondria and smooth endoplasmic reticulum,are widely distributed throughout the neuron,others are segregated to either the somatodendritic or axonal compartment.For example,Golgi outposts and acidified lysosomes are predominantly present in the somatodendritic domain and rarely distributed along the axon,whereas newly formed autophagosomes and synaptic vesicles are mainly distributed in the distal axon(Britt et al.,2016).

Landscape ecological risk assessment and its driving factors in the Weihe River basin,China

Weihe River basin is of great significance to analyze the changes of land use pattern and landscape ecological risk and to improve the ecological basis of regional development.Based on land use data of the Weihe River basin in 2000,2010,and 2020,with the support of Aeronautical Reconnaissance Coverage Geographic Information System(ArcGIS),GeoDa,and other technologies,this study analyzed the spatial-temporal characteristics and driving factors of land use pattern and landscape ecological risk.Results showed that land use structure of the Weihe River basin has changed significantly,with the decrease of cropland and the increase of forest land and construction land.In the past 20 a,cropland has decreased by 7347.70 km2,and cropland was mainly converted into forest land,grassland,and construction land.The fragmentation and dispersion of ecological landscape pattern in the Weihe River basin were improved,and land use pattern became more concentrated.Meanwhile,landscape ecological risk of the Weihe River basin has been improved.Severe landscape ecological risk area decreased by 19,177.87 km2,high landscape ecological risk area decreased by 3904.35 km2,and moderate and low landscape ecological risk areas continued to increase.It is worth noting that landscape ecological risks in the upper reaches of the Weihe River basin are still relatively serious,especially in the contiguous areas of high ecological risk,such as Tianshui,Pingliang,Dingxi areas and some areas of Ningxia Hui Autonomous Region.Landscape ecological risk showed obvious spatial dependence,and high ecological risk area was concentrated.Among the driving factors,population density,precipitation,normalized difference vegetation index(NDVI),and their interactions are the most important factors affecting the landscape ecological risk of the Weihe River basin.The findings significantly contribute to our understanding of the ecological dynamics in the Weihe River basin,providing crucial insights for sustainable management in the region.

A Novel Defender-Attacker-Defender Model for Resilient Distributed Generator Planning with Network Reconfiguration and Demand Response

To improve the resilience of a distribution system against extreme weather,a fuel-based distributed generator(DG)allocation model is proposed in this study.In this model,the DGs are placed at the planning stage.When an extreme event occurs,the controllable generators form temporary microgrids(MGs)to restore the load maximally.Simultaneously,a demand response program(DRP)mitigates the imbalance between the power supply and demand during extreme events.To cope with the fault uncertainty,a robust optimization(RO)method is applied to reduce the long-term investment and short-term operation costs.The optimization is formulated as a tri-level defenderattacker-defender(DAD)framework.At the first level,decision-makers work out the DG allocation scheme;at the second level,the attacker finds the optimal attack strategy with maximum damage;and at the third level,restoration measures,namely distribution network reconfiguration(DNR)and demand response are performed.The problem is solved by the nested column and constraint generation(NC&CG)method and the model is validated using an IEEE 33-node system.Case studies validate the effectiveness and superiority of the proposed model according to the enhanced resilience and reduced cost.

Stability of the Stratification of Water Bodies in the North Passage of the Yangtze River Estuary Based on the EFDC Model

To study the stratified stability of a water column in the North Passage of the Yangtze River Estuary,a numerical model of the hydrodynamics of this estuary is established using the EFDC model.On the basis of EFDC results,this paper derives and pro-vides the discriminative index of water body stability caused by salinity and analyzes the along-range variation in water body strati-fication stability in the North Passage of the Yangtze River Estuary and the periodic variation at a key location(bend area)based on the simulation results of the numerical model.This work shows that the water body in the bend area varies between mixed and strati-fied types,and the vertical average flow velocity has a good negative correlation with the differential velocity between the surface and bottom layers of the water body.The model simulation results validate the formulae for the stratified stability discriminant during spring tides.

Distributed Multicircular Circumnavigation Control for UAVs with Desired Angular Spacing

This paper addresses a multicircular circumnavigation control for UAVs with desired angular spacing around a nonstationary target.By defining a coordinated error relative to neighboring angular spacing,under the premise that target information is perfectly accessible by all nodes,a centralized circular enclosing control strategy is derived for multiple UAVs connected by an undirected graph to allow for formation behaviors concerning the moving target.Besides,to avoid the requirement of target’s states being accessible for each UAV,fixed-time distributed observers are introduced to acquire the state estimates in a fixed-time sense,and the upper boundary of settling time can be determined offline irrespective of initial properties,greatly releasing the burdensome communication traffic.Then,with the aid of fixed-time distributed observers,a distributed circular circumnavigation controller is derived to force all UAVs to collaboratively evolve along the preset circles while keeping a desired angular spacing.It is inferred from Lyapunov stability that all errors are demonstrated to be convergent.Simulations are offered to verify the utility of proposed protocol.

Projected impacts of climate change on the habitat of Xerophyta species in Africa

Climate change poses a serious long-term threat to biodiversity.To effectively reduce biodiversity loss,conservationists need to have a thorough understanding of the preferred habitats of species and the variables that affect their distribution.Therefore,predicting the impact of climate change on speciesappropriate habitats may help mitigate the potential threats to biodiversity distribution.Xerophyta,a monocotyledonous genus of the family Velloziaceae is native to mainland Africa,Madagascar,and the Arabian Peninsula.The key drivers of Xerophyta habitat distribution and preference are unknown.Using 308 species occurrence data and eight environmental variables,the MaxEnt model was used to determine the potential distribution of six Xerophyta species in Africa under past,current and future climate change scenarios.The results showed that the models had a good predictive ability(Area Under the Curve and True Skill Statistics values for all SDMs were more than 0.902),indicating high accuracy in forecasting the potential geographic distribution of Xerophyta species.The main bioclimatic variables that impacted potential distributions of most Xerophyta species were mean temperature of the driest quarter(Bio9)and precipitation of the warmest quarter(Bio18).According to our models,tropical Africa has zones of moderate and high suitability for Xerophyta taxa,which is consistent with the majority of documented species localities.The habitat suitability of the existing range of the Xerophyta species varied based on the climate scenario,with most species experiencing a range loss greater than the range gain regardless of the climate scenario.The projected spatiotemporal patterns of Xerophyta species help guide recommendations for conservation efforts.